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Machine Learning for Planetary Science

Machine Learning for Planetary Science
  • Author : Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner
  • Publisher :Unknown
  • Release Date :2021-03-01
  • Total pages :400
  • ISBN : 9780128187227
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Summary : Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice

Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences
  • Author : Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
  • Publisher :Unknown
  • Release Date :2017-08-01
  • Total pages :208
  • ISBN : 9781498703888
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Summary : From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

Machine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather
  • Author : Enrico Camporeale,Simon Wing,Jay Johnson
  • Publisher :Unknown
  • Release Date :2018-05-31
  • Total pages :454
  • ISBN : 9780128117897
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Summary : Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. Collects many representative non-traditional approaches to space weather into a single volume Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2020-09-25
  • Total pages :316
  • ISBN : 9780128216842
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Summary : Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more. Provides high-level reviews of the latest innovations in geophysics Written by recognized experts in the field Presents an essential publication for researchers in all fields of geophysics

Machine Learning Methods in the Environmental Sciences

Machine Learning Methods in the Environmental Sciences
  • Author : William W. Hsieh
  • Publisher :Unknown
  • Release Date :2009-07-30
  • Total pages :349
  • ISBN : 9780521791922
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Summary : A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

Earth Observation Open Science and Innovation

Earth Observation Open Science and Innovation
  • Author : Pierre-Philippe Mathieu,Christoph Aubrecht
  • Publisher :Unknown
  • Release Date :2018-01-23
  • Total pages :330
  • ISBN : 9783319656335
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Summary : This book is published open access under a CC BY 4.0 license. Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites. This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites.

Planetary Surface Processes

Planetary Surface Processes
  • Author : H. Jay Melosh
  • Publisher :Unknown
  • Release Date :2011-08-25
  • Total pages :229
  • ISBN : 9781139498302
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Summary : Planetary Surface Processes is the first advanced textbook to cover the full range of geologic processes that shape the surfaces of planetary-scale bodies. Using a modern, quantitative approach, this book reconsiders geologic processes outside the traditional terrestrial context. It highlights processes that are contingent upon Earth's unique circumstances and processes that are universal. For example, it shows explicitly that equations predicting the velocity of a river are dependent on gravity: traditional geomorphology textbooks fail to take this into account. This textbook is a one-stop source of information on planetary surface processes, providing readers with the necessary background to interpret new data from NASA, ESA and other space missions. Based on a course taught by the author at the University of Arizona for 25 years, it is aimed at advanced students, and is also an invaluable resource for researchers, professional planetary scientists and space-mission engineers.

Machine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather
  • Author : Enrico Camporeale,Simon Wing,Jay Johnson
  • Publisher :Unknown
  • Release Date :2018-06
  • Total pages :400
  • ISBN : 0128117885
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Summary : Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. Collects many representative non-traditional approaches to space weather into a single volume Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists Includes free software in the form of simple MATLAB(R) scripts that allow for replication of results in the book, also familiarizing readers with algorithms

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation
  • Author : Petr Skoda,Fathalrahman Adam
  • Publisher :Unknown
  • Release Date :2020-04-10
  • Total pages :472
  • ISBN : 9780128191552
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Summary : Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. Addresses both astronomy and geosciences in parallel, from a big data perspective Includes introductory information, key principles, applications and the latest techniques Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields

Machine Learning on Mars

Machine Learning on Mars
  • Author : Hannah Rae Kerner
  • Publisher :Unknown
  • Release Date :2019
  • Total pages :269
  • ISBN : OCLC:1199083630
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Summary : There are more than 20 active missions exploring planets and small bodies beyond Earth in our solar system today. Many more have completed their journeys or will soon begin. Each spacecraft has a suite of instruments and sensors that provide a treasure trove of data that scientists use to advance our understanding of the past, present, and future of the solar system and universe. As more missions come online and the volume of data increases, it becomes more difficult for scientists to analyze these complex data at the desired pace. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and prioritize the most promising, novel, or relevant observations for scientific analysis. Machine learning methods can serve this need in a variety of ways: by uncovering patterns or features of interest in large, complex datasets that are difficult for humans to analyze; by inspiring new hypotheses based on structure and patterns revealed in data; or by automating tedious or time-consuming tasks. In this dissertation, I present machine learning solutions to enhance the tactical planning process for the Mars Science Laboratory Curiosity rover and future tactically-planned missions, as well as the science analysis process for archived and ongoing orbital imaging investigations such as the High Resolution Imaging Science Experiment (HiRISE) at Mars. These include detecting novel geology in multispectral images and active nuclear spectroscopy data, analyzing the intrinsic variability in active nuclear spectroscopy data with respect to elemental geochemistry, automating tedious image review processes, and monitoring changes in surface features such as impact craters in orbital remote sensing images. Collectively, this dissertation shows how machine learning can be a powerful tool for facilitating scientific discovery during active exploration missions and in retrospective analysis of archived data.

The Sun, Interplanetary Medium, Earth's Magnetosphere and Planetary Sciences

The Sun, Interplanetary Medium, Earth's Magnetosphere and Planetary Sciences
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :2008
  • Total pages :233
  • ISBN : STANFORD:36105131918646
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Summary :

Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy
  • Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
  • Publisher :Unknown
  • Release Date :2012-03-29
  • Total pages :744
  • ISBN : 9781439841730
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Summary : Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Space Science and Public Engagement

Space Science and Public Engagement
  • Author : Amy Paige Kaminski
  • Publisher :Unknown
  • Release Date :2021-06-15
  • Total pages :298
  • ISBN : 9780128173916
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Summary : Space Science and Public Engagement: 21st Century Perspectives and Opportunities critically examines the many dimensions of public engagement with space science by exploring case studies that show a spectrum of public engagement formats, ranging from the space science community's efforts to communicate developments to the public, to citizenry attempting to engage with space science issues. It addresses why public engagement is important to space science experts, what approaches they take, how public engagement varies locally, nationally and internationally, and what roles "non-experts" have played in shaping space science. Space scientists, outreach specialists in various scientific disciplines, policymakers and citizens interested in space science will find great insights in this book that will help inform their future engagement strategies. Critically examines how expert organizations and the space science community have sought to bring space science to the public Examines how the public has responded, and in some cases self-organized, to opportunities to contribute to space science Outlines future engagement interests and possibilities

Lunar and Planetary Science

Lunar and Planetary Science
  • Author : Anonim
  • Publisher :Unknown
  • Release Date :1995
  • Total pages :229
  • ISBN : UIUC:30112026542677
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Summary :

Machine Learning with TensorFlow

Machine Learning with TensorFlow
  • Author : Chris Mattmann
  • Publisher :Unknown
  • Release Date :2020-12-23
  • Total pages :456
  • ISBN : 9781638350866
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Summary : Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x. About the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape

Planetary Remote Sensing and Mapping

Planetary Remote Sensing and Mapping
  • Author : Bo Wu,Kaichang Di,Jürgen Oberst,Irina Karachevtseva
  • Publisher :Unknown
  • Release Date :2018-10-29
  • Total pages :332
  • ISBN : 9780429000508
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Summary : The early 21st century marks a new era in space exploration. The National Aeronautics and Space Administration (NASA) of the United States, The European Space Agency (ESA), as well as space agencies of Japan, China, India, and other countries have sent their probes to the Moon, Mars, and other planets in the solar system. Planetary Remote Sensing and Mapping introduces original research and new developments in the areas of planetary remote sensing, photogrammetry, mapping, GIS, and planetary science resulting from the recent space exploration missions. Topics covered include: Reference systems of planetary bodies Planetary exploration missions and sensors Geometric information extraction from planetary remote sensing data Feature information extraction from planetary remote sensing data Planetary remote sensing data fusion Planetary data management and presentation Planetary Remote Sensing and Mapping will serve scientists and professionals working in the planetary remote sensing and mapping areas, as well as planetary probe designers, engineers, and planetary geologists and geophysicists. It also provides useful reading material for university teachers and students in the broader areas of remote sensing, photogrammetry, cartography, GIS, and geodesy.

AAAI 99

AAAI 99
  • Author : American Association for Artificial Intelligence
  • Publisher :Unknown
  • Release Date :1999
  • Total pages :998
  • ISBN : UCSD:31822027890540
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Summary : The annual AAAI National Conference provides a forum for information exchange and interaction among researchers from all disciplines of AI. Contributions include theoretical, experimental, and empirical results. Topics cover principles of cognition, perception, and action; the design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analyses of tasks and domains in which intelligent systems perform. Distributed for the AAAI Press

Machine Learning and Biometrics

Machine Learning and Biometrics
  • Author : Jucheng Yang,Dong Sun Park,Sook Yoon,Yarui Chen,Chuanlei Zhang
  • Publisher :Unknown
  • Release Date :2018-08-29
  • Total pages :146
  • ISBN : 9781789235906
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Summary : We are entering the era of big data, and machine learning can be used to analyze this deluge of data automatically. Machine learning has been used to solve many interesting and often difficult real-world problems, and the biometrics is one of the leading applications of machine learning. This book introduces some new techniques on biometrics and machine learning, and new proposals of using machine learning techniques for biometrics as well. This book consists of two parts: "Biometrics" and "Machine Learning for Biometrics." Parts I and II contain four and three chapters, respectively. The book is reviewed by editors: Prof. Jucheng Yang, Prof. Dong Sun Park, Prof. Sook Yoon, Dr. Yarui Chen, and Dr. Chuanlei Zhang.

Statistics of Earth Science Data

Statistics of Earth Science Data
  • Author : Graham J. Borradaile
  • Publisher :Unknown
  • Release Date :2013-11-11
  • Total pages :351
  • ISBN : 9783662052235
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Summary : From the reviews: "All in all, Graham Borradaile has written and interesting and idiosyncratic book on statistics for geoscientists that will be welcome among students, researchers, and practitioners dealing with orientation data. That should include engineering geologists who work with things like rock fracture orientation measurements or clast alignment in paleoseismic trenches. It won’t replace the collection of statistics and geostatistics texts in my library, but it will have a place among them and will likely be one of several references to which I turn when working with orientation data.... The text is easy to follow and illustrations are generally clear and easy to read..."(William C. Haneberg, Haneberg Geoscience)

Open Source Software Policy Options for NASA Earth and Space Sciences

Open Source Software Policy Options for NASA Earth and Space Sciences
  • Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Space Studies Board,Committee on Best Practices for a Future Open Code Policy for NASA Space Science
  • Publisher :Unknown
  • Release Date :2019-01-27
  • Total pages :108
  • ISBN : 9780309482714
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Summary : Modern science is ever more driven by computations and simulations. In particular, the state of the art in space and Earth science often arises from complex simulations of climate, space weather, and astronomical phenomena. At the same time, scientific work requires data processing, presentation, and analysis through broadly available proprietary and community software.1 Implicitly or explicitly, software is central to science. Scientific discovery, understanding, validation, and interpretation are all enhanced by access to the source code of the software used by scientists. This report investigates and recommends options for NASA's Science Mission Directorate (SMD) as it considers how to establish a policy regarding open source software to complement its existing policy on open data. In particular, the report reviews existing data and software policies and the lessons learned from the implementation of those policies, summarizes community perspectives, and presents policy options and recommendations for implementing an open source software policy for NASA SMD.

Using Machine Learning for Hydrocarbon Prospecting in Reconcavo Basin, Brazil

Using Machine Learning for Hydrocarbon Prospecting in Reconcavo Basin, Brazil
  • Author : Elezhan Zhakiya
  • Publisher :Unknown
  • Release Date :2016
  • Total pages :28
  • ISBN : OCLC:1031985504
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Summary : Machine Learning techniques are being widely used in Social Sciences to find connections amongst various variables. Machine Learning connects features across different fields that do not seem to have known mathematical relationships with each other. In natural resource prospecting, machine learning can be applied to connect geochemical, geophysical, and geological variables. However, the biggest challenge in machine learning remains obtaining the data to train the ML algorithms. Here, we have applied machine learning on data extracted from maps via image processing. While the overall accuracy of prediction remains as low as 33% at this stage, we see places where the algorithm can be improved and the accuracy increased.